Estimating PM2.5 Of Main Cities In Yangtze River Delta Based On Remote Sensing

Recent years, haze happens more and more frequently in China. PM2.5 is primary pollutant. Haze seriously threatened the life and health of local citizen, declines city’s visibility. PM2.5 draws more attention from public. Therefore, it has great significance to monitor the concentration of PM2.5.This paper chose nine cities in Yangtze River Delta as study area. Data sets include ground level PM2.5 concentration from June 1,2013 to May 30,2015; MODIS aerosol products and meteorological factors (temperature, relative humility, surface pressure, u wind, v wind, surface roughness, boundary layer height). This paper utilizes them to set up annual and seasonal estimation models of PM2.5 concentrations.PM2.5 in nine cities has obvious features. The hourly average PM2.5 concentration in Ningbo is lower than other cities. PM2.5 in Nanjing is highest. The diurnal variation of PM2.5 is not stable. The average concentration of PM2.5 is highest in November. Compare to other seasons, seasonal averaged concentration is higher in winter. From the analysis of meteorological factor with PM2.5, we can conclude that, temperature, relative humility and wind are the most significant three elements.This paper utilizes two methods to estimate PM2.5:step wise regression and single-hidden layer feedforward. Stepwise regression models involve ground level PM2.5, MODIS AOD and meteorological factors, R2 of every model is higher than 0.6. The highest R2 is 0.884. The lowest MAPE is 0.244. Single-hidden layer feedforward based estimation model shows the same PM2.5 trend between measured value and estimated value. This study shows that satellite remote sensing effectively estimates the ground level PM2.5 concentration. The air pollution was the heaviest and most long-lasting pollution event in the historical record of Shanghai from 30 November to 9 December,2013. The aim of this work was to analyze the characteristics and formation mechanisms of this episode. For this purpose, the air quality data, ground meteorological data and satellite remote sensing data were used.